{"ID":5935649,"CreatedAt":"2026-07-07T01:22:02.77346169Z","UpdatedAt":"2026-07-07T02:10:06.972658124Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2607.03512","arxiv_id":"2607.03512","title":"High-Precision Formation Control for Heterogeneous Multi-Robot Systems via Hierarchical Hybrid Physics-Informed Deep Reinforcement Learning","abstract":"Existing classical control methods commonly require precise models and struggle to cope with model uncertainties and external disturbances, while end-to-end reinforcement learning (RL) approaches suffer from low sample efficiency and poor convergence. To overcome these challenges, this paper proposes a hierarchical hybrid physics-informed deep reinforcement learning (HHy-PIDRL) framework, aiming to realize high-precision, highly responsive formation control for heterogeneous multi-robot systems (HMRSs). The proposed framework contains two layers. Specifically, first, the upper layer designs an autonomous navigation policy network for Ackermann-steering leader based on the Soft Actor-Critic (SAC) deep reinforcement learning (DRL) algorithm. Second, the lower module integrates a high-fidelity physical feed-forward controller, a classical proportional-derivative (PD) controller, and an adaptive DRL residual controller to propose an effective hybrid model and DRL (HM-DRL)-based formation control policy network. Third, a unique hierarchical reward function is designed for training Omnidirectional followers, which effectively guides agents toward a refined, stable control policy. Experimental results demonstrate that, the success rate of both the upper-layer autonomous navigation policy network and the HM-DRL based formation control policy networks reach 100%. Meanwhile, ablation experiments are conducted to verify the validity and credibility of the proposed method.","short_abstract":"Existing classical control methods commonly require precise models and struggle to cope with model uncertainties and external disturbances, while end-to-end reinforcement learning (RL) approaches suffer from low sample efficiency and poor convergence. To overcome these challenges, this paper proposes a hierarchical hyb...","url_abs":"https://arxiv.org/abs/2607.03512","url_pdf":"https://arxiv.org/pdf/2607.03512v1","authors":"[\"Yanzhou Li\",\"Guangli Chen\",\"Xiao-Meng Li\",\"Wenjian Zhong\",\"Yongkang Lu\",\"Shenghuang He\"]","published":"2026-07-03T17:38:17Z","proceeding":"cs.RO","tasks":"[\"cs.RO\"]","methods":"[\"Reinforcement Learning\"]","has_code":false}